Neuron Patching: Semantic-based Neuron-level Language Model Repair For Code Generation
Gu Jian, Aleti Aldeida, Chen Chunyang, Zhang Hongyu. Arxiv 2023
[Paper]
Large Language Models (LLMs) have already gained widespread adoption in software engineering, particularly in code generation tasks. However, updating these models with new knowledge can be prohibitively expensive, yet it is essential to maximize their utility, such as implementing a hotfix technique to address urgent or critical LLM errors. In this paper, we propose \textsc{MENT}, a novel and effective model editing approach to repair LLMs in coding tasks. \textsc{MENT} is effective, efficient, and reliable, capable of correcting a neural model by patching just one or two neurons. As pioneering work on neuron-level model editing of generative models, we formalize the editing process and introduce the involved concepts. We also introduce new measures to evaluate its generalization ability and establish a benchmark for further study. Our approach is evaluated on three coding tasks: line-level code generation, shellcode generation, and intent-to-bash translation. The experimental results demonstrate that the proposed approach significantly outperforms the state-of-the-art in both effectiveness and efficiency measures. Furthermore, we showcase the applications of \textsc{MENT} for LLM reasoning in software engineering. By editing LLM knowledge, the directly or indirectly dependent behaviors of API invocation in the chain-of-thought change accordingly. This illustrates the significance of repairing LLMs in the context of software engineering.
Similar Work